Predictive Analytics of Selected Datasets using DTREG Data Mining Tool

Predictive analytics is making a significant wave in healthcare Industry. Predictive analytics is an analytics offshoot which helps to make future predictions, resulting in more informed decisions. Data is central to accurate predictions. Several concepts like Data Mining, AI (Artificial Intelligence), Machine Learning and statistics need to work in tandem to ensure precise predictions. The main aim of the research work was to analyse the datasets of selected diseases using the DTREG data mining tool. Two datasets namely Alzheimer’s and Breast Cancer were taken from a public repository and analysed. Various algorithms namely single tree, decision tree, tree boost, support vector machine and neural network were studied. The results obtained were interpreted to understand which algorithm works best in each case. Also, the important predictors in each study were recorded. Interpretation of Alzheimer’s and breast cancer data using DTREG revealed neural network as the best algorithm. The significant predictors for Alzheimer’s were estimated as total intracranial blood volume, clinical dementia rating and age, and for breast cancer were uniformity of cell size, cell shape, benign and malignant and clump thickness. Data mining, artificial Intelligence and machine learning can thus be of very good help in determining the line of treatment to be followed by extracting knowledge from such suitable databases.